In the field of digital imaging, and more particularly medical imaging, objects of interest may not always be sufficiently visible, often due to two contributing factors. One is the presence of excessive noise. Noise is always present in medical images, such as Magnetic Resonance Imaging (MRI), Computerized Tomography (CT), ultrasound and radiographic (X-ray) images. In general, signal to noise ratio, and therefore, image visibility, may be improved with increased patient exposure or dose, or both. However, such increase often tends to adversely affect a patient's health and therefore is generally to be avoided whenever possible. There is thus a trade-off between image noise and patient exposure and dose.
Poor contrast in digital images, especially medical images, often is another contributing factor to the poor visibility of objects of interest in these images. The presence of both noise and poor contrast often further reduces the visibility of object structures in images. The contrast problem arises also because of the mismatch between the dynamic range of most medical imaging devices and the dynamic range of image rendering devices. Generally, the dynamic range of a digital image tends to be much larger than the dynamic range of most films and display devices. For example, a chest X-ray image has a very broad range of pixel values since X-rays are readily transmitted through the lungs but highly attenuated by the mediastinum. Consequently, it is difficult to simultaneously observe both the lungs and the mediastinum in a single image due to the limitation of display devices. Simplistic methods for compressing the dynamic range lead to low overall contrast and/or loss of subtle details. It is common to address this problem by allowing simple interactive adjustments of the contrast and brightness. But, such adjustments cannot usually produce acceptable contrast throughout the entire image simultaneously, and in any case they are considered too time consuming in clinical settings.
There is thus an urgent need to display digital images, in particular medical images, on film or computer screens, in such a way that all relevant details of an object of interest, such as details relevant to diagnosing diseases, are enhanced, namely simultaneously visible with acceptable contrast.
Many different image processing techniques have been developed to address this need, including histogram equalization, unsharp masking, and noise smoothing. However, such methods have had only limited success.
More recently, multiscale methods have been introduced and applied to the image enhancement problem, with significantly better results. However, the solutions proposed are not entirely satisfactory. For example, U.S. Pat. No. 5,461,655 discloses a multiscale method but is primarily concerned with noise reduction, not contrast or edge enhancement. According to this method, the noise is estimated using a local standard deviation, which tends to give unreliable results in a region containing edges of imaged objects, namely, in a region where image signal strength varies greatly. U.S. Pat. Nos. 5,467,404 and 5,805,721 describe contrast enhancement via non-linear lookup tables applied to each sub-band image in a multiscale decomposition. However, each pixel value is modified based on the pixel's current value alone, without regard to its location in an image nor the values of pixels in the neighborhood of the pixel. This method tends to enhance the noise as well as the signal, as it does not distinguish between the two. Similarly, U.S. Pat. No. 5,960,123 describes an algorithm very similar to that of U.S. Pat. Nos. 5,467,404 and 5,805,721. Hence the method disclosed therein also tends to enhance both the noise and the signal.
It is an object of the present invention to mitigate or obviate at least one of the above mentioned disadvantages.